An Effective Learning and Sentimental Analysis of Social Posts for Stress Level Identification

Sentiment analysis of social media posts is important in analyzing stress levels, providing analyzed reports of public mental health trends, and timely interferences. In recent years with the increasing online interactions, social media platforms offer a valuable source for understanding collective emotions. Lexicon-Based approaches, machine-learning models, and hybrid techniques are the current most used sentiment analysis techniques. Often missed contextual nuance and the words associated with emotions rely on Lexicon-Based methods and their predefined dictionaries of words. To overcome these challenges, Aspect-Based Sentiment Analysis remains the best approach. ABSA enables more granular insights by classifying stress into different aspects— such as relationships, work, health, and finances. This analysis targets specific areas of concern and improves public well-being by guiding mental health professionals with more precise interventions. Developing more efficient, context-aware models that can operate in real time can change the future of sentiment analysis. ABSA makes interventions more actionable and precise by focusing on specific areas of concern. As the study progresses hybrid models that combine the strengths of lexicon-based, machine-learning, and deep-learning techniques provide improved accuracy and scalability of sentiment analysis systems. Analyzing ethical concerns privacy and data security is vital for sentiment analysis because of its wide analysis and public mental health monitoring.